Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

Overview

DataSelection-NMT

Selecting Parallel In-domain Sentences for Neural Machine Translation Using Monolingual Texts

Quick update: The paper got accepted on Dec 6, 2021! I will link the repository to the paper as soon as it got published.

Our Pre-trained models on Hugging Face

Systems Link Systems Link
Top1 Download Top1 Download
Top2+Top1 Download Top2 Download
Top3+Top2+... Download Top3 Donwload
Top4+Top3+... Download Top4 Donwload
Top5+Top4+... Download Top5 Donwload
Top6+Top5+... Download Top6 Donwload

How to use

Note: we ported the best checkpoints of trained models to the Hugging Face (HF). Since our models were trained by OpenNMT-py, it was not possible to employ them directly for inference on HF. To bypass this issue, we use CTranslate2– an inference engine for transformer models.

Follow steps below to translate your sentences:

1. Install the Python package:

pip install --upgrade pip
pip install ctranslate2

2. Download models from our HF repository: You can do this manually or use the following python script:

import requests

url = "Download Link"
model_path = "Model Path"
r = requests.get(url, allow_redirects=True)
open(model_path, 'wb').write(r.content)

3. Convert the downloaded model:

ct2-opennmt-py-converter --model_path model_path --output_dir output_directory

3. Translate tokenized inputs:

Note: the inputs should be tokenized by SentencePiece. You can also use tokenized version of IWSLT test sets.

import ctranslate2
translator = ctranslate2.Translator("output_directory/")
translator.translate_batch([["▁H", "ello", "▁world", "!"]])

or

import ctranslate2
translator = ctranslate2.Translator("output_directory/")
translator.translate_file(input_file, output_file, batch_type= "tokens/examples")

To customize the CTranslate2 functions, read this API document.

4. Detokenize the outputs:

Note: you need to detokenize the output with the same sentencepiece model as used in step 3.

tools/detokenize.perl -no-escape -l fr \
< output_file \
> output_file.detok

5. Remove the @@ tokens:

cat output_file.detok | sed -E 's/(@@)|(@@ )|(@@ ?$)//g' \
> output._file.detok.postprocessd

Use grep to check if @@ tokens removed successfully:

grep @@ output._file.detok.postprocessd 

Authors

You might also like...
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Parallel Tacotron2 Pytorch Implementation of Google's Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling

Code for the paper
Code for the paper "JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design"

JANUS: Parallel Tempered Genetic Algorithm Guided by Deep Neural Networks for Inverse Molecular Design This repository contains code for the paper: JA

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-based API design, PyKale enforces standardization and minimalism, via reusing existing resources, reducing repetitions and redundancy, and recycling learning models across areas.

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

ood-text-emnlp Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them" Files fine_tune.py is used to finetune the GPT-2 mo

Generate images from texts. In Russian. In PaddlePaddle
Generate images from texts. In Russian. In PaddlePaddle

ruDALL-E PaddlePaddle ruDALL-E in PaddlePaddle. Install: pip install rudalle_paddle==0.0.1rc1 Run with free v100 on AI Studio. Original Pytorch versi

Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts

t5-japanese Codes to pre-train T5 (Text-to-Text Transfer Transformer) models pre-trained on Japanese web texts. The following is a list of models that

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph
Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph

Build an Amazon SageMaker Pipeline to Transform Raw Texts to A Knowledge Graph This repository provides a pipeline to create a knowledge graph from ra

Code for CVPR2021 "Visualizing Adapted Knowledge in Domain Transfer". Visualization for domain adaptation. #explainable-ai

Visualizing Adapted Knowledge in Domain Transfer @inproceedings{hou2021visualizing, title={Visualizing Adapted Knowledge in Domain Transfer}, auth

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation

[CVPR2021] Domain Consensus Clustering for Universal Domain Adaptation [Paper] Prerequisites To install requirements: pip install -r requirements.txt

Releases(1.1)
Owner
Javad Pourmostafa
PhD candidate in AI, an NLPer, working on NMT
Javad Pourmostafa
Compare outputs between layers written in Tensorflow and layers written in Pytorch

Compare outputs of Wasserstein GANs between TensorFlow vs Pytorch This is our testing module for the implementation of improved WGAN in Pytorch Prereq

Hung Nguyen 72 Dec 20, 2022
ICLR 2021, Fair Mixup: Fairness via Interpolation

Fair Mixup: Fairness via Interpolation Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predicti

Ching-Yao Chuang 49 Nov 22, 2022
Final report with code for KAIST Course KSE 801.

Orthogonal collocation is a method for the numerical solution of partial differential equations

Chuanbo HUA 4 Apr 06, 2022
Neural Tangent Generalization Attacks (NTGA)

Neural Tangent Generalization Attacks (NTGA) ICML 2021 Video | Paper | Quickstart | Results | Unlearnable Datasets | Competitions | Citation Overview

Chia-Hung Yuan 34 Nov 25, 2022
A coin flip game in which you can put the amount of money below or equal to 1000 and then choose heads or tail

COIN_FLIPPY ##This is a simple example package. You can use Github-flavored Markdown to write your content. Coinflippy A coin flip game in which you c

2 Dec 26, 2021
Zero-shot Learning by Generating Task-specific Adapters

Code for "Zero-shot Learning by Generating Task-specific Adapters" This is the repository containing code for "Zero-shot Learning by Generating Task-s

INK Lab @ USC 11 Dec 17, 2021
Code for the RA-L (ICRA) 2021 paper "SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition"

SeqNet: Learning Descriptors for Sequence-Based Hierarchical Place Recognition [ArXiv+Supplementary] [IEEE Xplore RA-L 2021] [ICRA 2021 YouTube Video]

Sourav Garg 63 Dec 12, 2022
STBP is a way to train SNN with datasets by Backward propagation.

Spiking neural network (SNN), compared with depth neural network (DNN), has faster processing speed, lower energy consumption and more biological interpretability, which is expected to approach Stron

Ling Zhang 18 Dec 09, 2022
ROMP: Monocular, One-stage, Regression of Multiple 3D People, ICCV21

Monocular, One-stage, Regression of Multiple 3D People ROMP, accepted by ICCV 2021, is a concise one-stage network for multi-person 3D mesh recovery f

Yu Sun 937 Jan 04, 2023
Official PyTorch implementation of SyntaSpeech (IJCAI 2022)

SyntaSpeech: Syntax-Aware Generative Adversarial Text-to-Speech | | | | 中文文档 This repository is the official PyTorch implementation of our IJCAI-2022

Zhenhui YE 116 Nov 24, 2022
A fast implementation of bss_eval metrics for blind source separation

fast_bss_eval Do you have a zillion BSS audio files to process and it is taking days ? Is your simulation never ending ? Fear no more! fast_bss_eval i

Robin Scheibler 99 Dec 13, 2022
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models Code accompanying CVPR'20 paper of the same title. Paper lin

Alex Damian 7k Dec 30, 2022
When BERT Plays the Lottery, All Tickets Are Winning

When BERT Plays the Lottery, All Tickets Are Winning Large Transformer-based models were shown to be reducible to a smaller number of self-attention h

Sai 16 Nov 10, 2022
Assessing syntactic abilities of BERT

BERT-Syntax Assesing the syntactic abilities of BERT. What Evaluate Google's BERT-Base and BERT-Large models on the syntactic agreement datasets from

Yoav Goldberg 147 Aug 02, 2022
Denoising images with Fourier Ring Correlation loss

Denoising images with Fourier Ring Correlation loss The python code accompanies the working manuscript Image quality measurements and denoising using

2 Mar 12, 2022
The official code of Anisotropic Stroke Control for Multiple Artists Style Transfer

ASMA-GAN Anisotropic Stroke Control for Multiple Artists Style Transfer Proceedings of the 28th ACM International Conference on Multimedia The officia

Six_God 146 Nov 21, 2022
Geometric Sensitivity Decomposition

Geometric Sensitivity Decomposition This repo is the official implementation of A Geometric Perspective towards Neural Calibration via Sensitivity Dec

16 Dec 26, 2022
PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners

Masked Autoencoders: A PyTorch Implementation This is a PyTorch/GPU re-implementation of the paper Masked Autoencoders Are Scalable Vision Learners: @

Meta Research 4.8k Jan 04, 2023
A framework for GPU based high-performance medical image processing and visualization

FAST is an open-source cross-platform framework with the main goal of making it easier to do high-performance processing and visualization of medical images on heterogeneous systems utilizing both mu

Erik Smistad 315 Dec 30, 2022
Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment".

#backdoor-HSIC (bd_HSIC) Accompanying code for the paper "A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment". To generate

Robert Hu 0 Nov 25, 2021